Articles | Volume 25, issue 24
https://doi.org/10.5194/acp-25-18549-2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
Influence of secondary ice formation on tropical deep convective clouds simulated by the Unified Model
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- Final revised paper (published on 19 Dec 2025)
- Supplement to the final revised paper
- Preprint (discussion started on 22 Aug 2025)
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Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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- RC1: 'Comment on egusphere-2025-3158', Anonymous Referee #1, 08 Sep 2025
- RC2: 'Comment on egusphere-2025-3158', Anonymous Referee #2, 10 Sep 2025
- RC3: 'Comment on egusphere-2025-3158', Pierre Grzegorczyk, 12 Sep 2025
- AC1: 'Comment on egusphere-2025-3158', Mengyu Sun, 10 Nov 2025
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AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Mengyu Sun on behalf of the Authors (10 Nov 2025)
Author's response
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ED: Referee Nomination & Report Request started (11 Nov 2025) by Jianzhong Ma
RR by Pierre Grzegorczyk (13 Nov 2025)
RR by Anonymous Referee #2 (24 Nov 2025)
ED: Publish as is (30 Nov 2025) by Jianzhong Ma
AR by Mengyu Sun on behalf of the Authors (06 Dec 2025)
Secondary ice production (SIP) is an uncertain process in different cloud types including the tropical deep convection. Incorporating multiple SIP mechanisms into the CASIM microphysics scheme, the results of a Hector thunderstorm show that SIP greatly increases ice number concentrations and ice water content, expands anvil cloud coverage, and modifies both radiation and rainfall patterns. These results highlight the need to represent SIP in cloud-resolving models to better capture tropical convection and its climatic impacts. As a strong positive, I appreciate the use of small modeling ensembles, which adds robustness to the results. The simulated cases are also well presented and compared with observations. However, the analysis remains somewhat limited and would benefit from greater depth. I recommend strengthening the analysis before the manuscript can be considered for publication.
Overall comments
In analyzing the mean values of different cloud properties, have you considered that the simulations may encompass different cloud volumes? Conditional sampling could introduce biases if only mean values are compared. In addition, the choice of modeling framework, along with the level of microphysical detail and spatial resolution, may strongly influence the results. These aspects should be discussed in greater depth.
Specific comments
Line 16: “…including droplet fragmentation (Mode 1 and Mode 2)…” Referring to the modes here is a technical detail that is not widely known. Please either provide a brief explanation of these modes or omit mentioning them from the Abstract.
Lines 74–75: Is the effect limited to increased ice loading, or is it possible that the anvil also extends over a larger area?
Section 2.2: A number of equations are presented, many of which appear to originate from earlier publications. If these are identical to previous formulations, please explain why they are repeated here. There is also an option to move these into supplementary material.
Lines 306–307: “…inclusion of secondary ice production leads to a reduction in mean reflectivity values in middle levels. This is mainly attributed to the smaller ice particles aloft.” What exactly does “mainly” mean here? Which hydrometeor categories are responsible for the change? Do all categories show decreased size and increased number, or is the effect limited to specific ones? Even if SIP generates new particles, those originating from primary freezing should still grow almost as fast as those without SIP if ice–ice collisional breakup is inefficient. Please clarify.
Lines 312–314: “Below 3 km, the model underestimates the frequency of reflectivity values exceeding 5 dBZ…” Does this mean that raindrops are evaporating too rapidly or are not large enough in the beginning, or could the discrepancy also stem from how reflectivity is calculated and conditionally sampled?
Lines 356–359: “The convective core is less pronounced…” Is it possible to get some statistics to support this. Visually this is not too evident.
Figure 8: How large is the uncertainty in the “observed” precipitation data?
Lines 485–486: “Ice–ice collisional breakup remains largely insignificant…” Why is the discussion restricted primarily to graupel? The process involves all ice hydrometeors. Given that the all-SIP simulation produces the highest ice concentrations, how can you rule out a contribution from collisional breakup after other processes increase concentrations?
Figure 10: Is the amount of snow reduced because of SIP? Not evident from the ice particle concentrations in Figure 9. Can this affect the outcome, for example related to changes in reflectivity?
Figure 10 (Maximum updraft velocity): Could you compare the simulated maximum updraft velocities with those from higher-resolution studies, e.g., large-eddy simulations by Dauhut et al. (2015)? The values here appear smaller than might be expected, which could have implications for SIP efficiency and overall precipitation formation.
Lines 569–570: “SIP diverts condensate away from warm-rain processes into less efficient ice-phase pathways.” Please clarify this statement. Ice particles typically grow faster than liquid droplets, so in what sense is the pathway “less efficient”?
Line 578: “…the updraft velocity (Wmax) increases by ~10%… and does not substantially intensify peak convection.” A 10% change in cloud dynamics due to microphysical processes could be considered significant. What threshold would you regard as “substantial”?
References
Dauhut, T., Chaboureau, J.-P., Escobar, J. and Mascart, P. (2015), Large-eddy simulations of Hector the convector making the stratosphere wetter. Atmos. Sci. Lett., 16: 135-140. https://doi.org/10.1002/asl2.534